R: Inverse probability weighted proportional cause-specific...

weight_cause_cox {cmprskcoxmsm}

R Documentation

Inverse probability weighted proportional cause-specific hazards model

Description

weight_cause_cox fits the marginal structural proportional cause-specific hazards model using the inverse probability treatment weights.

Usage

weight_cause_cox(data=,
                 time, time2 = NULL,
                 Event.var, Event,
                 weight.type,
                 ties = NULL)

Arguments

`data`	The dataset, output of `doPS`
`time`	See also `Surv`, for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.
`time2`	See also `Surv`, ending time of the interval for interval censored or counting process data only. Intervals are assumed to be open on the left and closed on the right, (start, end]. For counting process data, event indicates whether an event occurred at the end of the interval.
`Event.var`	The variable name for the event indicator which typically has at least 3 levels.
`Event`	Event of interest, the rest of the event are treating as competing event.
`weight.type`	Type of inverse probability weights. Possible values are "Unstabilized" and "Stabilized".
`ties`	See also `coxph`, a character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent.

Details

The marginal structural cause-specific Cox model for cause j usually has the form:

\lambda^{a}_j (t) \equiv \lambda_{T^{a},J^{a}=j}(t) = \lambda_{0j}e^{\beta*a},

where T^{a}, J^{a} is the counterfactural survival time and cause for treatment a (=0,1), \lambda_{0j} is the unspecified baseline cause-specific hazard for cause j, and \beta is the treatment effect.

Value